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Skeleton

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An Intelligent System for Detecting Abnormal Behavior in Students Based on the Human Skeleton and Deep Learning.

Computational intelligence and neuroscience
With the use of an intelligent video system, this research provides a method for detecting abnormal behavior based on the human skeleton and deep learning. To begin with, the spatiotemporal features of human bones are extracted through iterative trai...

Skeleton-Based Spatio-Temporal U-Network for 3D Human Pose Estimation in Video.

Sensors (Basel, Switzerland)
Despite the great progress in 3D pose estimation from videos, there is still a lack of effective means to extract spatio-temporal features of different granularity from complex dynamic skeleton sequences. To tackle this problem, we propose a novel, s...

A Lightweight Subgraph-Based Deep Learning Approach for Fall Recognition.

Sensors (Basel, Switzerland)
Falls pose a great danger to social development, especially to the elderly population. When a fall occurs, the body's center of gravity moves from a high position to a low position, and the magnitude of change varies among body parts. Most existing f...

Locomotion of an untethered, worm-inspired soft robot driven by a shape-memory alloy skeleton.

Scientific reports
Soft, worm-like robots show promise in complex and constrained environments due to their robust, yet simple movement patterns. Although many such robots have been developed, they either rely on tethered power supplies and complex designs or cannot mo...

Fast Temporal Graph Convolutional Model for Skeleton-Based Action Recognition.

Sensors (Basel, Switzerland)
Human action recognition has a wide range of applications, including Ambient Intelligence systems and user assistance. Starting from the recognized actions performed by the user, a better human-computer interaction can be achieved, and improved assis...

A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.

Sensors (Basel, Switzerland)
Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale da...

Self-Supervised Action Representation Learning Based on Asymmetric Skeleton Data Augmentation.

Sensors (Basel, Switzerland)
Contrastive learning has received increasing attention in the field of skeleton-based action representations in recent years. Most contrastive learning methods use simple augmentation strategies to construct pairs of positive samples. When using such...

Skeleton-Based Human Pose Recognition Using Channel State Information: A Survey.

Sensors (Basel, Switzerland)
With the increasing demand for human-computer interaction and health monitoring, human behavior recognition with device-free patterns has attracted extensive attention. The fluctuations of the Wi-Fi signal caused by human actions in a Wi-Fi coverage ...

A Bioinspired Fluid-Filled Soft Linear Actuator.

Soft robotics
In bioinspired soft robotics, very few studies have focused on fluidic transmissions and there is an urgent need for translating fluidic concepts into realizable fluidic components to be applied in different fields. Nature has often offered an inspir...

Skeleton-Based Human Motion Prediction With Privileged Supervision.

IEEE transactions on neural networks and learning systems
Existing supervised methods have achieved impressive performance in forecasting skeleton-based human motion. However, they often rely on action class labels in both training and inference phases. In practice, it could be a burden to request action cl...